Approximating multi-objective scheduling problems
Type de document :
Article dans une revue scientifique: Article original
Titre :
Approximating multi-objective scheduling problems
Auteur(s) :
Dabia, Said [Auteur]
Department of Industrial Engineering and Innovation Sciences
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
van Woensel, Tom [Auteur]
Department of Industrial Engineering and Innovation Sciences
de Kok, Ton [Auteur]
Department of Industrial Engineering and Innovation Sciences
Department of Industrial Engineering and Innovation Sciences
Talbi, El-Ghazali [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
van Woensel, Tom [Auteur]
Department of Industrial Engineering and Innovation Sciences
de Kok, Ton [Auteur]
Department of Industrial Engineering and Innovation Sciences
Titre de la revue :
Computers and Operations Research
Pagination :
1165-1175
Éditeur :
Elsevier
Date de publication :
2013-05
ISSN :
0305-0548
Mot(s)-clé(s) en anglais :
Multi-objective decisions
State-dependent costs
Approximation
Dynamic programming
State-dependent costs
Approximation
Dynamic programming
Discipline(s) HAL :
Computer Science [cs]/Operations Research [math.OC]
Résumé en anglais : [en]
In many practical situations, decisions are multi-objective by nature. In this paper, we propose a generic approach to deal with multi-objective scheduling problems (MOSPs). The aim is to determine the set of Pareto solutions ...
Lire la suite >In many practical situations, decisions are multi-objective by nature. In this paper, we propose a generic approach to deal with multi-objective scheduling problems (MOSPs). The aim is to determine the set of Pareto solutions that represent the interactions between the different objectives. Due to the complexity of MOSPs, an efficient approximation based on dynamic programming is developed. The approximation has a provable worst case performance guarantee. Even though the approximate Pareto set consists of fewer solutions, it represents a good coverage of the true set of Pareto solutions. We consider generic cost parameters that depend on the state of the system. Numerical results are presented for the time-dependent multi-objective knapsack problem, showing the value of the approximation in the special case when the state of the system is expressed in terms of time.Lire moins >
Lire la suite >In many practical situations, decisions are multi-objective by nature. In this paper, we propose a generic approach to deal with multi-objective scheduling problems (MOSPs). The aim is to determine the set of Pareto solutions that represent the interactions between the different objectives. Due to the complexity of MOSPs, an efficient approximation based on dynamic programming is developed. The approximation has a provable worst case performance guarantee. Even though the approximate Pareto set consists of fewer solutions, it represents a good coverage of the true set of Pareto solutions. We consider generic cost parameters that depend on the state of the system. Numerical results are presented for the time-dependent multi-objective knapsack problem, showing the value of the approximation in the special case when the state of the system is expressed in terms of time.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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